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Foods
Special Issues
Emerging Challenges in the Management of Food Safety and Authenticity
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Emerging Challenges in the Management of Food Safety and Authenticity

A special issue of Foods (ISSN 2304-8158). This special issue belongs to the section "Food Quality and Safety".

Deadline for manuscript submissions: 21 June 2025 | Viewed by 3977

Special Issue Editor

Prof. Dr. Angela Di Pinto

E-Mail Website
Guest Editor
Department of Veterinary Medicine, Food Safety Section, University of Bari Aldo Moro, 70010 Valenzano, Bari, Italy
Interests: food safety; molecular DNA-based analytical techniques; food microbiology; food fraud
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In an era characterised by rapid technological advances and the introduction of novel food sources, new food safety challenges have emerged along global food supply chains. Therefore, there is an ongoing need to evaluate new food safety risks, including biological threats such as pathogens, or chemical contaminants such as heavy metals, pesticides, drugs, hormones, and allergens. Furthermore, the development of preventive and effective control measures, as well as robust authentication and traceability systems, are crucial for designing innovative food safety management systems (FSMS). These systems are vital for safeguarding the interests of both producers and consumers, as well as for supporting industry stakeholders and food safety authorities.

This Special Issue, therefore, is a compilation of original research papers and review articles that address emerging issues regarding food safety, authenticity, and traceability. Our goal is to provide a platform for evaluating any new safety risks posed by pathogens, chemical contaminants, and the undeclared introduction of potentially harmful compounds into food ingredients. Additionally, this issue aims to explore measures for preventing food fraud, and for enhancing authentication and traceability. These efforts seek to protect human health and the interests of commercial consumers, and to align with the Global Strategy for Food Safety.

Prof. Dr. Angela Di Pinto
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Foods is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • food safety management system
  • food safety
  • food traceability
  • food authentication
  • food pathogens
  • food chemical contaminants
  • analytical approaches

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

22 pages, 1664 KiB  
Article
Environmental and Food Safety Assessment of Pre-Harvest Activities in Local Small-Scale Fruit and Vegetable Farms in Northwest Portugal: Hazard Identification and Compliance with Good Agricultural Practices (GAPs)
by Ariana Macieira, Virgínia Cruz Fernandes, Teresa R. S. Brandão, Cristina Delerue-Matos and Paula Teixeira
Foods 2025, 14(12), 2129; https://doi.org/10.3390/foods14122129 - 18 Jun 2025
Viewed by 173
Abstract
The popularity of small-scale and local fruit and vegetable production has increased in recent years due to perceived economic, environmental, and social benefits. However, these operations face contamination risks that both consumers and small-scale producers may underestimate. The present study aimed to assess [...] Read more.
The popularity of small-scale and local fruit and vegetable production has increased in recent years due to perceived economic, environmental, and social benefits. However, these operations face contamination risks that both consumers and small-scale producers may underestimate. The present study aimed to assess the microbiological and chemical hazards on fruit, vegetables, soil, and water samples from small-scale farms in north-western Portugal during pre-harvest activities. Additionally, the study investigated farmers’ non-compliance with food safety regulations and good agricultural practices (GAPs), exploring how their behaviour might contribute to the identified hazards. A before-and-after analysis of non-compliant behaviours was conducted to determine the impact of training on improving food safety practices. The analysis identified the presence of pathogenic bacteria, pesticides, flame retardant residues, nitrates, and heavy metals. Lead (Pb) concentrations exceeded EU limits in organic carrots from one producer (0.156 ± 0.043 mg/kg) and in chard from another (0.450 ± 0.126 mg/kg). Cadmium (Cd) levels were also above regulatory thresholds in bell peppers (0.023 ± 0.009 mg/kg) and organic tomatoes (0.026 ± 0.015 mg/kg) from two different producers. Elevated levels of heavy metals were detected in irrigation water from two sites, with zinc (Zn) at 0.2503 ± 0.0075 mg/L and Pb at 0.0218 ± 0.0073 mg/L. Among food samples, the most prevalent microorganisms were Pseudomonas spp. (88.2%), Bacillus cereus (76.5%), and aerobic mesophilic bacteria (100%). Phosphorus flame retardants (PFRs), particularly tris(2-butoxyethyl) phosphate (TBEP), were detected in all food and soil samples. Some EU-banned pesticides were detected in food and soil samples, but at levels below the maximum residue limits (MRLs). Chlorpyrifos (35.3%) and p,p’-DDD (23.5%) were the most detected pesticides in food samples. After the training, GAP behaviour improved, particularly that related to hygiene. However, issues related to record-keeping and soil and water analyses persisted, indicating ongoing challenges in achieving full compliance. Full article
(This article belongs to the Special Issue Emerging Challenges in the Management of Food Safety and Authenticity)
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12 pages, 3040 KiB  
Article
Authentication of Edible Oil by Real-Time One Class Classification Modeling
by Min Liu, Xueyan Wang, Yong Yang, Fengqin Tu, Li Yu, Fei Ma, Xuefang Wang, Xiaoming Jiang, Xinjing Dou, Peiwu Li and Liangxiao Zhang
Foods 2025, 14(7), 1235; https://doi.org/10.3390/foods14071235 - 1 Apr 2025
Viewed by 508
Abstract
Adulteration detection or authentication is considered a type of one-class classification (OCC) in chemometrics. An effective OCC model requires representative samples. However, it is challenging to collect representative samples from all over the world. Moreover, it is also very hard to evaluate the [...] Read more.
Adulteration detection or authentication is considered a type of one-class classification (OCC) in chemometrics. An effective OCC model requires representative samples. However, it is challenging to collect representative samples from all over the world. Moreover, it is also very hard to evaluate the representativeness of collected samples. In this study, we blazed a new trail to propose an authentication method to identify adulterated edible oils without building a prediction model beforehand. An authentication method developed by real-time one-class classification modeling, and model population analysis was designed to identify adulterated oils in the market without building a classification model beforehand. The underlying philosophy of the method is that the sum of the absolute centered residual (ACR) of the good model built by only authentic samples is higher than that of the bad model built by authentic and adulterated samples. In detail, a large number of OCC models were built by selecting partial samples out of inspected samples using Monte Carlo sampling. Then, adulterated samples involved in the test of these good models were identified. Taking the inspected samples of avocado oils as an example, as a result, 6 out of 40 avocado oils were identified as adulterated and then validated by chemical markers. The successful identification of avocado oils adulterated with soybean oil, corn oil, or rapeseed oil validated the effectiveness of our method. The proposed method provides a novel idea for oils as well as other high-value food adulteration detection. Full article
(This article belongs to the Special Issue Emerging Challenges in the Management of Food Safety and Authenticity)
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Graphical abstract

18 pages, 2130 KiB  
Article
Integrated Quality Prediction Model for Food Quality Management Based on E. coli in Shared Kitchens
by Taeyeoun Roh, Youngchul Song and Byungun Yoon
Foods 2024, 13(24), 4065; https://doi.org/10.3390/foods13244065 - 17 Dec 2024
Viewed by 956
Abstract
Shared kitchens have a lower entry barrier than traditional kitchens, which generally require a significant initial investment, and have thus attracted attention as the most realistic new business model for restaurants in the sharing economy. The restaurant industry is founded on ensuring the [...] Read more.
Shared kitchens have a lower entry barrier than traditional kitchens, which generally require a significant initial investment, and have thus attracted attention as the most realistic new business model for restaurants in the sharing economy. The restaurant industry is founded on ensuring the safety of the food it serves in order to prevent the spread of foodborne diseases within the community, so strict quality control is essential. Existing food quality management typically employs continuous quality assistance, which is difficult to apply to the highly volatile shared kitchen environment and its various stakeholders. Therefore, in this study, a predictive model for managing food quality that can monitor volatility using quantitative indicators, especially microbial counts, is proposed. Stakeholder- and quality-related factors associated with shared kitchens are first defined, then a modified Gompertz growth curve and the transfer rate equation are used to quantify them. The proposed model, utilizing E. coli as a practical indicator for easily measuring changes in general environments, can be used to systematically manage food quality within the shared kitchen industry, thus supporting the establishment of this new business model. Full article
(This article belongs to the Special Issue Emerging Challenges in the Management of Food Safety and Authenticity)
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13 pages, 965 KiB  
Article
Decoding Seafood: Multi-Marker Metabarcoding for Authenticating Processed Seafood
by Anna Mottola, Roberta Piredda, Lucilia Lorusso, Lucia Ranieri, Chiara Intermite, Concettina Barresi, Carmela Galli and Angela Di Pinto
Foods 2024, 13(15), 2382; https://doi.org/10.3390/foods13152382 - 27 Jul 2024
Cited by 2 | Viewed by 1656
Abstract
Given the recognized nutritional value of fish and shifting consumer lifestyles, processed seafood has become increasingly prevalent, comprising a significant portion of global food production. Although current European Union labeling regulations do not require species declaration for these products, food business operators often [...] Read more.
Given the recognized nutritional value of fish and shifting consumer lifestyles, processed seafood has become increasingly prevalent, comprising a significant portion of global food production. Although current European Union labeling regulations do not require species declaration for these products, food business operators often voluntarily provide this information on ingredient lists. Next Generation Sequencing (NGS) approaches are currently the most effective methods for verifying the accuracy of species declarations on processed seafood labels. This study examined the species composition of 20 processed seafood products, each labeled as containing a single species, using two DNA metabarcoding markers targeting the mitochondrial cytochrome c oxidase I (COI) and 16S rRNA genes. The combined use of these markers revealed that the majority of the products contained multiple species. Furthermore, two products were found to be mislabeled, as the declared species were not detected. These findings underscore that NGS is a robust technique that could be adopted to support routine food industry activities and official control programs, thereby enhancing the ‘From Boat to Plate’ strategy and combating fraudulent practices in the complex fisheries supply chain. Full article
(This article belongs to the Special Issue Emerging Challenges in the Management of Food Safety and Authenticity)
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